Production Machine Learning Systems

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  • Introduction to Advanced Machine Learning on Google Cloud
    • This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
  • Architecting Production ML Systems
    • This module explores what else a production ML system needs to do and how to meet those needs. You review how to make important, high-level, design decisions around training and model serving need to make in order to get the right performance profile for your model.
  • Designing Adaptable ML Systems
    • In this module, you learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
  • Designing High-Performance ML Systems
    • In this module, you identify performance considerations for machine learning models.
      Machine learning models are not all identical. For some models, you focus on improving I/O performance, and on others, you focus on squeezing out more computational speed.
  • Building Hybrid ML Systems
    • Understand the tools and systems available and when to leverage hybrid machine learning models.
  • Summary
    • This module reviews what you learned in this course.